Data Mining Trauma: AI-Assisted Qualitative Study of Cyber Victimization on Reddit
Bibliographic record
Abstract
Background: Cyber victimization exposes individuals to numerous risks. Developmental and psychological factors may leave some users unaware of the potential dangers, increasing their susceptibility to psychological distress. Despite this vulnerability, methods for identifying those at risk of cyber victimization within health care settings are limited, as is research that explores their experiences of cyber victimization. The purpose of this study was to analyze how users describe experiences of cyber victimization on the social media platform Reddit (Reddit, Inc) using data mining. Objective: This study aimed to analyze and describe how users on Reddit describe and discuss their experience of cyber victimization using data mining and computational analysis of unsolicited data. Methods: This computational qualitative study used data mining, Word Adjacency Graph (WAG) modeling, and thematic analysis to analyze discussions of Reddit users surrounding cyber victimization. Inclusion criteria included posts from 2012 to 2023 from subreddits r/cyberbullying and r/bullying. GPT-4 (OpenAI), an advanced artificial intelligence language model, summarized posts and assisted in cluster labeling. Posts were reviewed to remove irrelevant content and duplicates. User anonymity was maintained throughout the study. Results: A total of 13,381 posts from 3283 Reddit were analyzed, with approximately 5.1% (n=678) originating between 2012 and 2018 and 94.9% (n=12,703) from 2019 to 2023. The WAG modeling approach identified 38 clusters, with 35 deemed to be relevant to cyber victimization experiences. Two clusters containing irrelevant material were excluded. Six overarching themes emerged: (1) psychological impact, (2) coping and healing, (3) protecting yourself online, (4) protecting yourself offline, (5) victimization across various settings, and (6) seeking meaning and understanding. Conclusions: The study highlights the effectiveness of data mining and AI in analyzing large public datasets for qualitative research. These methods can inform future studies on risky internet behavior, victimization, and assessment strategies in health care settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".